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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Tang, Jinhui Fu, Yusun Qiao, Yue Yin, Junpeng Huang, Haobo Shanghai Jiao Tong University Ningbo Artificial Intelligence Institute Ningbo China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350363760

The decoding success rate may be worse when classical degree distributions are used in short-packet transmission of rateless codes, especially in industrial communication scenarios. A novel Boltzmann degree distribution is proposed for Luby Transform (LT) codes. The Boltzmann degree distribution has a high decoding success rate for short-packet transmission but a lower decoding success rate in long-packet transmission. To balance the performance of rateless codes for short and long packet transmission, the Boltzmann degree distribution and the robust soliton distribution (RSD) are combined with a new joint degree distribution, called Boltzmann Robust Soliton Distribution (BRSD). Simulation results show that compared with RSD and Poisson robust soliton distribution (PRSD), the proposed joint distribution can achieve a higher decoding success rate and reduce the average degree, especially in short-packet transmission of industrial communication. © 2024 IEEE.

关键词： Decoding

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Semi-Supervised Transfer Boosting (SS-TrBoosting)

arXiv

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arXiv 2024年

作者： Deng, Lingfei Zhao, Changming Du, Zhenbang Xia, Kun Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University Science and Technology Research Institute Shenzhen518063 China

Semi-supervised domain adaptation (SSDA) aims at training a high-performance model for a target domain using few labeled target data, many unlabeled target data, and plenty of auxiliary data from a source domain. Previous works in SSDA mainly focused on learning transferable representations across domains. However, it is difficult to find a feature space where the source and target domains share the same conditional probability distribution. Additionally, there is no flexible and effective strategy extending existing unsupervised domain adaptation (UDA) approaches to SSDA settings. In order to solve the above two challenges, we propose a novel fine-tuning framework, semi-supervised transfer boosting (SS-TrBoosting). Given a well-trained deep learning-based UDA or SSDA model, we use it as the initial model, generate additional base learners by boosting, and then use all of them as an ensemble. More specifically, half of the base learners are generated by supervised domain adaptation, and half by semi-supervised learning. Furthermore, for more efficient data transmission and better data privacy protection, we propose a source data generation approach to extend SS-TrBoosting to semi-supervised source-free domain adaptation (SS-SFDA). Extensive experiments showed that SS-TrBoosting can be applied to a variety of existing UDA, SSDA and SFDA approaches to further improve their performance. © 2024, CC BY.

关键词： Self-supervised learning

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Explicit Predictive control of Forward DC-DC Converter Based on an Input-Mapping Method

Explicit Predictive Control of Forward DC-DC Converter Based...

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第43届中国控制会议

作者： Xiang Gu Dewei Li Aoyun Ma Yaru Yu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the practical application of model predictive control in power electronics, both online computational burden and model accuracy are crucial. This paper presents an explicit model predictive control method based on input-mapping *** address the issue of parameter drift in the forward DC-DC converter system, the input-mapping method is introduced to compensate for model deviations using historical data. Firstly, the average model of the forward DC-DC converter system is introduced. For the control objective of voltage tracking, an explicit predictive control using the incremental model is ***, input-mapping method is integrated with explicit predictive control. During offline computation, analytical forms of the input-mapping combination coefficients and the optimal solution function of explicit model predictive control are *** computation of combination coefficients is conducted to obtain the optimal solution rapidly. The effectiveness of the proposed method is verified through simulations in MATLAB.

关键词： Forward DC-DC converter explicit model predictive control input-mapping method

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Fire Detection Based on Improved Feature Fusion 13

Fire Detection Based on Improved Feature Fusion

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Cao, Xinrong Wu, Jincai Li, Zuoyong College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Sichuang Technology Co. Ltd. Fuzhou350100 China

ISBN: (纸本)9798350319156

Fire is a serious disaster that endangers life and property worldwide. Accurate and prompt fire detection can provide early warning information for firefighting efforts. Deep learning-based object detection algorithms have proven to be useful in the field of fire detection. However, fire detection is a particularly challenging task, due to the low-resolution of fire images and the presence of blurring and deformation in the targets. Existing fire detection methods are limited by poor detection accuracy and slow speed in complex fire scenarios. To address these challenges, we propose a fire detection algorithm based on improved YOLOv5s. Firstly, the attention module is embedded into the network structure. We introduce this attention module into the last layer of the backbone to enhance feature extraction ability. Secondly, we modify the Neck structure to fully fuse different feature maps. Through fusing extra low-level features from the backbone, it utilizes various receptive fields to enhance the expression of feature. Thirdly, SIoU is introduced to achieve faster convergence during the training. SIoU take into account the direction of the mismatch to redefine penalty indicators. The experimental results show that our proposed algorithm outperforms commonly used models. Even compared to specific fire detectors, our model exhibits certain advantages. © 2023 IEEE.

关键词： Fires

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yichong Li Xiaojian Liu Fan Cheng Xiaoyong Pan Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions.

关键词： Proteins Protein engineering Accuracy RNA Machine learning Predictive models Data models Gene expression Bioinformatics Diseases

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A New Multi-Scale Model for Medium-Term Load Forecasting

A New Multi-Scale Model for Medium-Term Load Forecasting

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Chinese control and Decision Conference, CCDC

作者： Peng Liu Hongliang Fang Jiang-Wen Xiao Yan-Wu Wang Xiao-Kang Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Ministry of Education Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Wuhan China

When a large number of distributed power and renewable energy are connected to the smart grid, the volatility and intermittency of renewable energy bring some challenges to the smart grid. Therefore, accurate medium-term load forecasting is essential because it is conducive to the stability of the power grid and can provide data support for the power generating plan. Many factors affect medium-term load forecasting and the real-time electricity price is a very important factor among them. In this paper, a multi-scale model based on LSTM model is proposed to extracts features from 3 different time scales including half-hourly time scale, daily time scale and monthly time scale. At first, the half-hourly data is processed by a half-hourly data processing layer to extract the half-hourly features. Then, its output is concatenated with the daily load data and is input into a daily data processing layer. Finally, the daily features are concatenated with the monthly load data and they are input into a monthly data processing layer to extract the monthly features and get the final forecasting results. The case study results demonstrate that the proposed multi-scale model has better performance than the single-scale models.

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection 37

A Multi-Kernel Principal Component Analysis Method for Quali...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Mu, Lingxia Lei, Biyu Liu, Ding Xi'An University of Technology School of Automation and Information Engineering Xi'an China Xi'An University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

ISBN: (纸本)9781665465366

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction. © 2022 IEEE.

关键词： Fault detection

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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Computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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Motor imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces

arXiv

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arXiv 2024年

作者： Wu, Huanyu Li, Siyang Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science Technology Research Institute Shenzhen518063 China

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user's MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, which consists of two modules: a prescreening module to screen MI trials out of the resting-state, and a classification module for MI classification. Both modules are trained with supervised learning followed by self-supervised learning, which refines the feature extractors. Within-subject and cross-subject asynchronous MI classifications on four different EEG datasets validated the effectiveness of SWPC, i.e., it always achieved the highest average classification accuracy, and outperformed the best state-of-the-art baseline on each dataset by about 2%. © 2024, CC BY.

关键词： Self-supervised learning

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RP-MSTC*: Multi-Agent Coverage Path Planning Algorithm Emphasizing Local Area Updates

RP-MSTC*: Multi-Agent Coverage Path Planning Algorithm Empha...

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Neuromorphic Computing (ICNC), International Conference on

作者： Yong Yang Dingxin He Huantao Mao Hong Chen Hongtao Wu Zhi-Wei Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China

Compared to traditional area coverage algorithms, this paper proposes a re-plannable multi-agent coverage path planning algorithm to address issues such as low coverage rate of multiple agents in large-scale scenarios like warehouses and inability to cope with local environmental changes. Initially, the MSTC * algorithm is utilized to create the coverage path based on the available map information. To address scenarios like idle areas for material placement, the method introduces local area detection and path re-planning to enhance the coverage rate. This paper verifies the feasibility and effectiveness of the algorithm under different maps through simulation experiments. Compared with the coverage situation of other coverage path planning algorithms in environments with local area updates, the algorithm proposed in this paper has a higher coverage rate in this scenario.

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